biglasso

In biglasso: Extending Lasso Model Fitting to Big Data

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 6, fig.height = 4)

Small Data

Linear regression

library(biglasso)

data(colon)
X <- colon$X
y <- colon$y
dim(X)

X[1:5, 1:5]

# convert X to a big.matrix object
# X.bm is a pointer to the data matrix
X.bm <- as.big.matrix(X)
str(X.bm)

dim(X.bm)

Note that this is the same as X[1:5, 1:5]:

X.bm[1:5, 1:5]

# fit entire solution path
fit <- biglasso(X.bm, y)
plot(fit)

Cross-Validation

To run cross-validation, use cv.biglasso():

cvfit <- cv.biglasso(X.bm, y, seed = 1234, nfolds = 10)

After cross-validation, a few things we can do:

• plot the cross-validation plots:

par(mfrow = c(2, 2), mar = c(3.5, 3.5, 3, 1), mgp = c(2.5, 0.5, 0))
plot(cvfit, type = "all")

• Summarize CV object:

summary(cvfit)

• Extract non-zero coefficients at the optimal $\lambda$ value:

coef(cvfit)[which(coef(cvfit) != 0), ]

Logistic Regression

data(Heart)
X <- Heart$X
y <- Heart$y
X.bm <- as.big.matrix(X)
fit <- biglasso(X.bm, y, family = "binomial")
plot(fit)

Cox Regression

library(survival)
X <- heart[, 4:7]
y <- Surv(heart$stop - heart$start, heart$event)
X.bm <- as.big.matrix(X)
fit <- biglasso(X.bm, y, family = "cox")
plot(fit)

Multiple response Linear Regression (multitask learning)

set.seed(10101)
n <- 300
p <- 300
m <- 5
s <- 10
b <- 1
x <- matrix(rnorm(n * p), n, p)
beta <- matrix(seq(from = -b, to = b, length.out = s * m), s, m)
y <- x[, 1:s] %*% beta + matrix(rnorm(n * m, 0, 1), n, m)
x.bm <- as.big.matrix(x)
fit <- biglasso(x.bm, y, family = "mgaussian")
plot(fit)

Big Data

When the raw data file is very large, it's better to convert the raw data file into a file-backed big.matrix by using a file cache. We can call function setupX, which reads the raw data file and creates a backing file (.bin) and a descriptor file (.desc) for the raw data matrix:

# The data has 200 observations, 600 features, and 10 non-zero coefficients.
# This is not actually very big, but vignettes in R are supposed to render
# quickly, so we'll have to pretend that this can't fit in memory.
# Much larger data can be handled in the same way.
if (!file.exists("BigX.bin")) {
  X <- matrix(rnorm(1000 * 5000), 1000, 5000)
  beta <- c(-5:5)
  y <- as.numeric(X[, 1:11] %*% beta)
  write.csv(X, "BigX.csv", row.names = FALSE)
  write.csv(y, "y.csv", row.names = FALSE)
  X.bm <- setupX("BigX.csv", header = TRUE)
}

It's important to note that the above operation is only a one-time execution. Once done, the data can be retrieved seamlessly by attaching its descriptor file (.desc) in any new R session:

rm(list = c("X", "X.bm", "y")) # Pretend starting a new session
X.bm <- attach.big.matrix("BigX.desc")
y <- read.csv("y.csv")[, 1]

This is very appealing for big data analysis in that we don't need to "read" the raw data again in a R session, which would be very time-consuming. The code below again fits a lasso-penalized linear model, and runs 10-fold cross-validation:

system.time({
  fit <- biglasso(X.bm, y)
})

plot(fit)

For large data sets, it is often convenient to run cross validation in parallel (you can certainly use more cores on your machine, but CRAN vignettes are only allowed to use 2):

cvfit <- cv.biglasso(X.bm, y, seed = 1234, ncores = 2, nfolds = 10)

par(mfrow = c(2, 2), mar = c(3.5, 3.5, 3, 1), mgp = c(2.5, 0.5, 0))
plot(cvfit, type = "all")

Links

• biglasso on CRAN
• biglasso on GitHub
• biglasso website
• big.matrix manipulation

biglasso documentation built on Aug. 25, 2026, 5:08 p.m.